Event chain.py

From Werner KRAUTH

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-This page presents the program markov_disks_box.py, a Markov-chain algorithm for four disks in a square box of sides 1.+This page presents the program event_chain.py, a Markov-chain algorithm for four disks in a square box of sides 1 with periodic boundary conditions.
__FORCETOC__ __FORCETOC__
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=Program= =Program=
- 
- import random 
-  
- L = [[0.25, 0.25], [0.75, 0.25], [0.25, 0.75], [0.75, 0.75]] 
- sigma = 0.15 
- sigma_sq = sigma ** 2 
- delta = 0.1 
- n_steps = 1000 
- for steps in range(n_steps): 
- a = random.choice(L) 
- b = [a[0] + random.uniform(-delta, delta), a[1] + random.uniform(-delta, delta)] 
- min_dist = min((b[0] - c[0]) ** 2 + (b[1] - c[1]) ** 2 for c in L if c != a) 
- box_cond = min(b[0], b[1]) < sigma or max(b[0], b[1]) > 1.0 - sigma 
- if not (box_cond or min_dist < 4.0 * sigma ** 2): 
- a[:] = b 
- print L 
- 
-=Version= 
-See history for version information. 
- 
-[[Category:Python]] 
import random, math import random, math
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a[dirc] = (a[dirc] + event_min) % 1.0 a[dirc] = (a[dirc] + event_min) % 1.0
distance_to_go -= event_min distance_to_go -= event_min
 +
 +=Version=
 +See history for version information.
 +
 +[[Category:Python]] [[Category:Honnef_2015]]

Revision as of 22:04, 22 September 2015

This page presents the program event_chain.py, a Markov-chain algorithm for four disks in a square box of sides 1 with periodic boundary conditions.


Contents

Description

Program

import random, math

def event(a, b, dirc, sigma):
    d_perp = abs(b[not dirc] - a[not dirc]) % 1.0
    d_perp = min(d_perp, 1.0 - d_perp)
    if d_perp > 2.0 * sigma:
        return float("inf")
    else:
        d_para = math.sqrt(4.0 * sigma ** 2 - d_perp ** 2)
        return (b[dirc] - a[dirc] - d_para + 1.0) % 1.0

L = [[0.25, 0.25], [0.25, 0.75], [0.75, 0.25], [0.75, 0.75]]
ltilde = 0.819284; sigma = 0.15
for iter in xrange(20000):
    dirc = random.randint(0, 1)
    print iter, dirc, L
    distance_to_go = ltilde
    next_a = random.choice(L)
    while distance_to_go > 0.0:
        a = next_a
        event_min = distance_to_go
        for b in [x for x  in L if x != a]:
            event_b = event(a, b, dirc, sigma)
            if event_b < event_min:
                next_a = b
                event_min = event_b
        a[dirc] = (a[dirc] + event_min) % 1.0
        distance_to_go -= event_min

Version

See history for version information.

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